training.py

# CNN model training
count = 0
loss_list = []
iteration_list = []
accuracy_list = []
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        
        train = Variable(images.view(100,1,28,28))
        labels = Variable(labels)
        # Clear gradients
        optimizer.zero_grad()
        # Forward propagation
        outputs = model(train)
        # Calculate softmax and ross entropy loss
        loss = error(outputs, labels)
        # Calculating gradients
        loss.backward()
        # Update parameters
        optimizer.step()
        
        count += 1
        if count % 50 == 0:
            # Calculate Accuracy         
            correct = 0
            total = 0
            # Iterate through test dataset
            for images, labels in test_loader:
                
                test = Variable(images.view(100,1,28,28))
                # Forward propagation
                outputs = model(test)
                # Get predictions from the maximum value
                predicted = torch.max(outputs.data, 1)[1]
                
                # Total number of labels
                total += len(labels)
                correct += (predicted == labels).sum()
            
            accuracy = 100 * correct / float(total)
            
            # store loss and iteration
            loss_list.append(loss.data)
            iteration_list.append(count)
            accuracy_list.append(accuracy)
        if count % 500 == 0:
            # Print Loss
            print('Iteration: {}  Loss: {}  Accuracy: {} %'.format(count, loss.data, accuracy))